A Three-Tier Deep Learning-Based Channel Access Method for WiFi Networks
Future WiFi networks require a channel access method that provides users with high capacity. Such a method must consider 1) channel bonding, which improves the transmission capacity of Access Points (APs); and 2) spatial reuse, where APs tune their Clear Channel Assessment (CCA) threshold and transm...
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| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Transactions on Machine Learning in Communications and Networking |
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| Online Access: | https://ieeexplore.ieee.org/document/10158058/ |
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| author | Yiwei Huang Kwan-Wu Chin |
| author_facet | Yiwei Huang Kwan-Wu Chin |
| author_sort | Yiwei Huang |
| collection | DOAJ |
| description | Future WiFi networks require a channel access method that provides users with high capacity. Such a method must consider 1) channel bonding, which improves the transmission capacity of Access Points (APs); and 2) spatial reuse, where APs tune their Clear Channel Assessment (CCA) threshold and transmit power in order to transmit concurrently with neighboring APs. To date, there are no solutions that <italic>jointly</italic> optimize the channels used by an AP, and the CCA threshold and transmit power of a bonded channel. To this end, we outline a three-tier deep learning approach. Briefly, at Layer-1, it selects a set of transmitting channels. At Layer-2 and Layer-3, it respectively determines the transmit power and CCA threshold for each selected channel. An AP then employs deep reinforcement learning to learn the optimal policy for each layer given its interference intensity and queue length. The simulation results show that when compared to three competing solutions, an AP that uses our approach is able to reduce its queue length by up to 62.52% under realistic traffic load. |
| format | Article |
| id | doaj-art-7cc11298f3094bcbb99ff4bd801ea1ab |
| institution | DOAJ |
| issn | 2831-316X |
| language | English |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Transactions on Machine Learning in Communications and Networking |
| spelling | doaj-art-7cc11298f3094bcbb99ff4bd801ea1ab2025-08-20T02:59:29ZengIEEEIEEE Transactions on Machine Learning in Communications and Networking2831-316X2023-01-0119010610.1109/TMLCN.2023.328809010158058A Three-Tier Deep Learning-Based Channel Access Method for WiFi NetworksYiwei Huang0https://orcid.org/0000-0001-6556-346XKwan-Wu Chin1https://orcid.org/0000-0003-1547-9272School of Electrical, Computer and Telecommunications Engineering, University of Wollongong, Wollongong, NSW, AustraliaSchool of Electrical, Computer and Telecommunications Engineering, University of Wollongong, Wollongong, NSW, AustraliaFuture WiFi networks require a channel access method that provides users with high capacity. Such a method must consider 1) channel bonding, which improves the transmission capacity of Access Points (APs); and 2) spatial reuse, where APs tune their Clear Channel Assessment (CCA) threshold and transmit power in order to transmit concurrently with neighboring APs. To date, there are no solutions that <italic>jointly</italic> optimize the channels used by an AP, and the CCA threshold and transmit power of a bonded channel. To this end, we outline a three-tier deep learning approach. Briefly, at Layer-1, it selects a set of transmitting channels. At Layer-2 and Layer-3, it respectively determines the transmit power and CCA threshold for each selected channel. An AP then employs deep reinforcement learning to learn the optimal policy for each layer given its interference intensity and queue length. The simulation results show that when compared to three competing solutions, an AP that uses our approach is able to reduce its queue length by up to 62.52% under realistic traffic load.https://ieeexplore.ieee.org/document/10158058/Medium accesscapacityMarkov decision processinterferencechannel aggregation |
| spellingShingle | Yiwei Huang Kwan-Wu Chin A Three-Tier Deep Learning-Based Channel Access Method for WiFi Networks IEEE Transactions on Machine Learning in Communications and Networking Medium access capacity Markov decision process interference channel aggregation |
| title | A Three-Tier Deep Learning-Based Channel Access Method for WiFi Networks |
| title_full | A Three-Tier Deep Learning-Based Channel Access Method for WiFi Networks |
| title_fullStr | A Three-Tier Deep Learning-Based Channel Access Method for WiFi Networks |
| title_full_unstemmed | A Three-Tier Deep Learning-Based Channel Access Method for WiFi Networks |
| title_short | A Three-Tier Deep Learning-Based Channel Access Method for WiFi Networks |
| title_sort | three tier deep learning based channel access method for wifi networks |
| topic | Medium access capacity Markov decision process interference channel aggregation |
| url | https://ieeexplore.ieee.org/document/10158058/ |
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